Visual object recognition is one of the most poorly understood mental faculties. Theories of object recognition are underspecified with respect to both the functional roles ascribed to different neural structures in the inferior- temporal (IT) cortex and the range of visual recognition behaviors exhibited by humans. The inadequacies of current theories stem from a theoretical status quo combined with methodological limitations inherent in both psychophysics and neurophysiology. To better understand object recognition we must discard both standard feed-forward, hierarchical models that bear little resemblance to the facts as we know them and the behavioral studies that test such narrow models. We must also develop new neuroimaging methods to complement neurophysiology, which severely under-samples object representation space and typically relies on ad hoc/a theoretic strategies for determining which features/objects yield maximal neural responses. This proposal introduces new tools for mapping feature and object selectivity across human visual cortex using functional Magnetic Resonance Imaging (fMRI). The effort is motivated by neurophysiological studies with similar objectives. However, the informativeness and generality of visual physiology has been limited by the low number of samples (~103 recordings) relative to the size of the neural representational space (~109 neurons). fMRI, which measures brain responses in voxels (~106 neurons), enables the study of neural codes at a macro level, yet at a resolution fine enough to capture meaningful functional differences between brain regions. To explore the feature selectivity of localized regions of IT, visual stimulation will be driven by real-time fMRI, in which accruing neural contrasts between conditions are computed instantaneously. This mapping approach will be enhanced by employing two principled strategies for moving through feature space: an a priori method that relies on an algorithm for automatically segmenting objects into features (which has been validated against human segmentations); and, an a posteriori method that relies on "mutual information" to identify features that carry more or less task-relevant information. The end result, a more complete and theoretically-driven picture of selectivity in the ventral pathway, will form the basis for a new model of visual object recognition. Two model assumptions will be tested using novel fMRI methods. First, reverse correlation ("superstitious perception") will be combined with trial-by-trial neural responses to assess whether object processing proceeds in a non-hierarchical manner in which larger numbers of relatively simple features (e.g., those encoded in V4) are combined in a non-linear manner to represent objects. Second, time-resolved fMRI will be used to examine the degree to which recognition is driven by top-down, context-dependent processes. An improved functional picture of IT in combination with a more refined model of visual object recognition will aid in the creation of more effective treatments and retraining strategies for individuals suffering from traumatic brain injury or life-long recognition impairments, including the face recognition deficits associated with Autism. This research will provide a much clearer (and systematic) picture of how the human brain creates the experience of object perception given the optical information arriving at our eyes. A better understanding of the neural mechanisms underlying object and face recognition may lead to more effective treatment and retraining strategies for individuals suffering from either traumatic brain injury, particularly to the brain structures supporting perception, or from life-long face recognition deficits ("congenital faceblindness"). Progress in this area may also afford more focused interventions for specific symptoms associated with certain developmental disease processes (e.g., impaired face recognition in Autism).